





'''simple docstring'''






import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
					import torch

if is_vision_available():
					import PIL


lowerCAmelCase									=   logging.get_logger(__name__)






def   __A    (				a_    :	np.ndarray   ,a_    :	Union[int, Iterable[int]]   ,a_    :	bool   ,a_    :	int						):
		def constraint_to_multiple_of(a_    :	Union[str, Any]   ,a_    :	Tuple   ,a_    :	List[str]=0   ,a_    :	Dict=None						):
				lowerCAmelCase  :     List[Any]       						= round(val / multiple						) * multiple

				if max_val is not None and x > max_val:
						lowerCAmelCase  :     Tuple       						= math.floor(val / multiple						) * multiple

				if x < min_val:
						lowerCAmelCase  :     Union[str, Any]       						= math.ceil(val / multiple						) * multiple

				return x

		lowerCAmelCase  :     Optional[Any]       						= (output_size, output_size) if isinstance(a_   ,a_						) else output_size

		lowerCAmelCase						,		lowerCAmelCase  :     Dict       						= get_image_size(a_						)
		lowerCAmelCase						,		lowerCAmelCase  :     Any       						= output_size

		# determine new height and width
		lowerCAmelCase  :     Union[str, Any]       						= output_height / input_height
		lowerCAmelCase  :     str       						= output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width						) < abs(1 - scale_height						):
						# fit width
						lowerCAmelCase  :     Optional[int]       						= scale_width
				else:
						# fit height
						lowerCAmelCase  :     int       						= scale_height

		lowerCAmelCase  :     List[Any]       						= constraint_to_multiple_of(scale_height * input_height   ,multiple=a_						)
		lowerCAmelCase  :     Union[str, Any]       						= constraint_to_multiple_of(scale_width * input_width   ,multiple=a_						)

		return (new_height, new_width)

class 		lowerCamelCase	(   _A							):
	snake_case_   = ["pixel_values"]



	def __init__( self		,   a_ = True		,   a_ = None		,   a_ = PILImageResampling.BILINEAR		,   a_ = False		,   a_ = 1		,   a_ = True		,   a_ = 1 / 255		,   a_ = True		,   a_ = None		,   a_ = None		,   **a_		,   ):
			super().__init__(**a_						)
			lowerCAmelCase  :     Tuple       						= size if size is not None else {"height": 384, "width": 384}
			lowerCAmelCase  :     Dict       						= get_size_dict(a_						)
			lowerCAmelCase  :     Any       						= do_resize
			lowerCAmelCase  :     int       						= size
			lowerCAmelCase  :     str       						= keep_aspect_ratio
			lowerCAmelCase  :     Optional[Any]       						= ensure_multiple_of
			lowerCAmelCase  :     Tuple       						= resample
			lowerCAmelCase  :     Union[str, Any]       						= do_rescale
			lowerCAmelCase  :     List[str]       						= rescale_factor
			lowerCAmelCase  :     List[str]       						= do_normalize
			lowerCAmelCase  :     int       						= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			lowerCAmelCase  :     Union[str, Any]       						= image_std if image_std is not None else IMAGENET_STANDARD_STD



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = False		,   a_ = 1		,   a_ = PILImageResampling.BICUBIC		,   a_ = None		,   **a_		,   ):
			lowerCAmelCase  :     Dict       						= get_size_dict(a_						)
			if "height" not in size or "width" not in size:
					raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''						)
			lowerCAmelCase  :     Optional[int]       						= get_resize_output_image_size(
			    a_		,   output_size=(size["height"], size["width"])		,   keep_aspect_ratio=a_		,   multiple=a_		,   )
			return resize(a_		,   size=a_		,   resample=a_		,   data_format=a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_ = None		,   **a_		,   ):
			return rescale(a_		,   scale=a_		,   data_format=a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_		,   a_		,   a_ = None		,   **a_		,   ):
			return normalize(a_		,   mean=a_		,   std=a_		,   data_format=a_		,   **a_						)



	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = None		,   a_ = ChannelDimension.FIRST		,   **a_		,   ):
			lowerCAmelCase  :     Any       						= do_resize if do_resize is not None else self.do_resize
			lowerCAmelCase  :     Optional[Any]       						= size if size is not None else self.size
			lowerCAmelCase  :     Optional[Any]       						= get_size_dict(a_						)
			lowerCAmelCase  :     List[str]       						= keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
			lowerCAmelCase  :     Union[str, Any]       						= ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
			lowerCAmelCase  :     List[str]       						= resample if resample is not None else self.resample
			lowerCAmelCase  :     Optional[int]       						= do_rescale if do_rescale is not None else self.do_rescale
			lowerCAmelCase  :     Optional[Any]       						= rescale_factor if rescale_factor is not None else self.rescale_factor
			lowerCAmelCase  :     Union[str, Any]       						= do_normalize if do_normalize is not None else self.do_normalize
			lowerCAmelCase  :     List[str]       						= image_mean if image_mean is not None else self.image_mean
			lowerCAmelCase  :     Tuple       						= image_std if image_std is not None else self.image_std

			lowerCAmelCase  :     Optional[Any]       						= make_list_of_images(a_						)

			if not valid_images(a_						):
					raise ValueError(
					    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
					    "torch.Tensor, tf.Tensor or jax.ndarray."						)

			if do_resize and size is None or resample is None:
					raise ValueError("Size and resample must be specified if do_resize is True."						)

			if do_rescale and rescale_factor is None:
					raise ValueError("Rescale factor must be specified if do_rescale is True."						)

			if do_normalize and (image_mean is None or image_std is None):
					raise ValueError("Image mean and std must be specified if do_normalize is True."						)

			# All transformations expect numpy arrays.
			lowerCAmelCase  :     List[str]       						= [to_numpy_array(a_						) for image in images]

			if do_resize:
					lowerCAmelCase  :     int       						= [self.resize(image=a_		,   size=a_		,   resample=a_						) for image in images]

			if do_rescale:
					lowerCAmelCase  :     Optional[Any]       						= [self.rescale(image=a_		,   scale=a_						) for image in images]

			if do_normalize:
					lowerCAmelCase  :     int       						= [self.normalize(image=a_		,   mean=a_		,   std=a_						) for image in images]

			lowerCAmelCase  :     Optional[Any]       						= [to_channel_dimension_format(a_		,   a_						) for image in images]

			lowerCAmelCase  :     Any       						= {"pixel_values": images}
			return BatchFeature(data=a_		,   tensor_type=a_						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_ = None						):
			lowerCAmelCase  :     List[str]       						= outputs.logits

			# Resize logits and compute semantic segmentation maps
			if target_sizes is not None:
					if len(a_						) != len(a_						):
							raise ValueError(
							    "Make sure that you pass in as many target sizes as the batch dimension of the logits"						)

					if is_torch_tensor(a_						):
							lowerCAmelCase  :     Any       						= target_sizes.numpy()

					lowerCAmelCase  :     str       						= []

					for idx in range(len(a_						)						):
							lowerCAmelCase  :     str       						= torch.nn.functional.interpolate(
							    logits[idx].unsqueeze(dim=0						)		,   size=target_sizes[idx]		,   mode="bilinear"		,   align_corners=a_						)
							lowerCAmelCase  :     Optional[int]       						= resized_logits[0].argmax(dim=0						)
							semantic_segmentation.append(a_						)
			else:
					lowerCAmelCase  :     Any       						= logits.argmax(dim=1						)
					lowerCAmelCase  :     List[str]       						= [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]						)]

			return semantic_segmentation










'''simple docstring'''






from collections import defaultdict

from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst






def   __A    (				):
		lowerCAmelCase						,		lowerCAmelCase  :     List[Any]       						= 9, 1_4  # noqa: F841
		lowerCAmelCase  :     int       						= [
		    [0, 1, 4],
		    [0, 7, 8],
		    [1, 2, 8],
		    [7, 8, 7],
		    [7, 6, 1],
		    [2, 8, 2],
		    [8, 6, 6],
		    [2, 3, 7],
		    [2, 5, 4],
		    [6, 5, 2],
		    [3, 5, 1_4],
		    [3, 4, 9],
		    [5, 4, 1_0],
		    [1, 7, 1_1],
		]

		lowerCAmelCase  :     Optional[Any]       						= defaultdict(a_						)
		for nodea, nodea, cost in edges:
				adjancency[nodea].append([nodea, cost]						)
				adjancency[nodea].append([nodea, cost]						)

		lowerCAmelCase  :     int       						= mst(a_						)

		lowerCAmelCase  :     Tuple       						= [
		    [7, 6, 1],
		    [2, 8, 2],
		    [6, 5, 2],
		    [0, 1, 4],
		    [2, 5, 4],
		    [2, 3, 7],
		    [0, 7, 8],
		    [3, 4, 9],
		]

		for answer in expected:
				lowerCAmelCase  :     List[str]       						= tuple(answer[:2]						)
				lowerCAmelCase  :     int       						= tuple(edge[::-1]						)
				assert edge in result or reverse in result



